Predictive adaptive front lighting algorithm for branching road geometry

ABSTRACT

A system for predictive front lighting of a vehicle. The system includes at least two headlamps, at least two swivel mechanisms and a controller. The first and second headlamps project a beam pattern for illumination of the vehicle path. The controller receives vehicle position data, as well as, data regarding a most likely path and a secondary path of the vehicle. The controller then calculates the desired swivel angle of the headlamps based on the most likely path of the vehicle and the secondary path of the vehicle, and the swivel mechanisms respectively manipulate each of the headlamps to accordingly change alignment of the headlamps based on the swivel angle for each of the of the headlamps.

BACKGROUND

1. Field of the Invention

The present invention generally relates to a system for adapting frontlighting in branched road geometries.

2. Description of Related Art

Conventional Adaptive Front Lighting (AFS) uses the steering angle andthe vehicle speed as the core data to estimate the desired swivel angleof the headlamps. Accordingly, the system only reacts to the sensedinstantaneous path of the vehicle. In some instances, however, theinstantaneous vehicle path is not entirely indicative of road path infront of the vehicle or even the desired road path. This may often betrue just before the vehicle enters or exits a curve, during a changingradius of curvature curve, or an S-curve scenario. Often it may beadvantageous for the headlamps' performance to illuminate the upcomingroad (leading the way) when it varies from the current vehicle path. Forexample, this may be desirable where the road branches or at anintersection. Therefore, lighting provided by conventional AFS systemswould not be optimal in these scenarios. Another disadvantage ofconventional AFS systems is their inability to predict the road geometryahead. This can be a particular problem when approaching branches,on/off ramps, or intersections.

In view of the above, it is apparent that there exists a need for animproved adaptive front lighting system for motor vehicles.

SUMMARY

In satisfying the above need, as well as overcoming the enumerateddrawbacks and other limitations of the related art, the presentinvention provides an improved adaptive front lighting system for avehicle. According to the present invention, the system predictsupcoming vehicle path data to optimally manipulate the beam from theheadlamps.

The system includes at least two headlamps, at least two swivelmechanisms, and a controller. The headlamps cooperate to project a beampattern for illumination of the vehicle path. The swivel mechanisms eachrespectively manipulate a headlamp to change the swivel angle of theheadlamps.

In operation, the controller receives vehicle position data, for examplefrom a GPS system. The controller may access a map database and use thevehicle position data to identify a map location of the vehicle. Thecontroller analyzes the map to determine a most likely path, based onthe map location and other vehicle heading parameters. The controllermay also identify a secondary path in the road geometry. The controllerthen calculates the desired swivel angles of the first and secondheadlamps based on the most likely path and the secondary path andcauses the headlamps to be appropriately moved so that the most likelyvehicle path is illuminated.

The first desired swivel angle may be calculated based on the branchingangle of the most likely path, a distance to branch, and a time to reachthe branch. Similarly, the second desired swivel angle may be calculatedbased on the branching angle of the secondary path, a distance to branchand a time to reach the branch. Further, the controller may beconfigured to calculate a nominal swivel angle such that a kink point(further discussed below) of the first headlamp beam will be apredefined distance from a host vehicle lane center. Accordingly, thecontroller may calculate a weighted average angle of the first desiredswivel angle and the nominal swivel angle. The headlamp associated withthe most likely path is then manipulated based on the weighted average.

Further objects, features and advantages of this invention will becomereadily apparent to persons skilled in the art after a review of thefollowing description, with reference to the drawings and claims thatare appended to and form a part of this specification.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view of an adaptive front lighting system for avehicle and embodying the principles of the present invention;

FIG. 2A is an overhead view of a curved entry scenario;

FIG. 2B is an overhead view of a curved exit scenario;

FIG. 2C is an overhead view of the S-curve scenario;

FIG. 2D is an overhead view of a lane change scenario;

FIG. 3 is an overhead view illustrating the effect of the swivel angleon the beam pattern;

FIG. 4 is a block diagram of a method for determining a desired swivelangle;

FIG. 5 is a flowchart of a method for calculating the desired swivelangle.

FIG. 6 is a graphical representation of vehicle path map data;

FIG. 7 is an overhead view illustrating the calculation of the desiredswivel angle; and

FIG. 8A-D are examples illustrating the maximum deviation point forvarious curved scenarios.

DETAILED DESCRIPTION

Referring now to FIG. 1, a system embodying the principles of thepresent invention is illustrated therein and generally designated at 10.The system 10 includes a controller 12, at least two headlamps 14, 16and at least two swivel mechanisms 18, 20 each respectively mounted toone of the headlamps 14, 16. A controller 12 receives input from one ormore of a GPS system 22, a yaw rate sensor 24, and a vehicle speedsensor 26. The controller 12 is configured to predict a most likelyvehicle path based on a stored map database and the various inputs fromthe GPS system 22, the yaw rate sensor 24, and/or the vehicle speedsensor 26. Based on the predicted path, the controller 12 manipulatesthe swivel mechanisms 18, 20 to respectively direct the headlamps 14, 16for illumination of the predicted vehicle path. The swivel mechanisms18, 20 may take many forms, including, for example, a linear drive forcontrolling motion that is attached to one side of the headlamp causingit to swivel about a pivot point or a drive for direct rotationalcontrol. Regardless of the implementation, the swivel mechanisms 18, 20manipulate a projected position of the light beam from the headlamps 14,16 in front of the vehicle. This manipulation may be in a common planeor in more than one plane.

The controller 12 predicts the upcoming vehicle path, assuming a singlepath, to optimally swivel the headlamps' beam. As noted above, in manyscenarios, the current vehicle heading or steering parameters may notaccurately indicate the future vehicle path and/or optimal illuminationpattern for the driver.

In the scenarios shown in FIGS. 2A-D, a conventional AFS system, aspreviously described, fails to swivel the headlamps to the optimalposition for the future vehicle path. In FIG. 2A, a curve entry scenariois shown. In this scenario, the vehicle 30 is driven on a straight road32 just prior to a curve 34. The vehicle yaw, as well as, the steeringwheel angle would seem to indicate that the vehicle path would bestraight in front of the vehicle 30, as denoted by arrow 36.Accordingly, a conventional AFS system would illuminate the areadirectly in front the vehicle 30. This may cause the headlamps to glareat oncoming traffic. However, it is desirable for the headlamps to startto swivel ahead of the curve to improve the visibility of the curveahead and to avoid glare for the oncoming traffic, as denoted by arrow38.

In FIG. 2B, a curve exit scenario is shown. The vehicle 40 is driven ona curved road 42 just prior to exiting a curve 44 in this scenario. Thevehicle yaw, as well as the steering wheel angle, indicates that thevehicle 40 is currently turning at a given turn angle. Accordingly, aconventional AFS system would illuminate an area in front of the vehicle40, but offset by the turn angle, as denoted by arrow 46. It isdesirable in this scenario that the headlamps start to swivel back tothe straight position before the end of the curve to improve thevisibility of the straight road segment ahead and to avoid glare for theoncoming traffic, as denoted by arrow 48.

In FIG. 2C, an S-curve scenario is shown. Here, the vehicle 50 is drivenon a road 52 through an S-curve 54. The vehicle yaw, as well as, thesteering wheel angle indicate that the vehicle 50 is currently turningat a given turn angle. Further, for the example shown, the yaw rateindicates the vehicle 50 is turning to the left. Accordingly, aconventional AFS system would illuminate an area in front of thevehicle, but offset to the left by the turn angle, as denoted by arrow56. Not only would the headlamps glare the oncoming traffic, but theheadlamps would provide illumination that is directed to the left of thevehicle 50 when the future vehicle path is to the right of the vehicle50. Therefore, it is desirable that the headlamps start to swivel beforethe vehicle 50 reaches the saddle point in the direction of the upcomingcurve so as to improve the visibility of the curve ahead and to avoidglare for the oncoming traffic, as denoted by arrows 58.

Finally, a lane change scenario is shown in FIG. 2D. Ideally, the system10 will keep the headlamps' beam parallel to the road, as denoted byarrow 69 while the vehicle 60 is driven along a straight segment of road62 and changes from a first lane 64 to a second lane 66. The vehicleyaw, as well as the steering wheel angle, indicate that the vehicle 60is currently turning at a given turn angle, as denoted by arrow 68. Thegeneral path of the vehicle 60, however, is straight ahead and should beilluminated as shown by arrow 69.

A conventional AFS system cannot achieve the above desired performancesbecause it uses the instantaneous (at the vehicle current position)steering angle and vehicle speed measurements to manipulate theheadlamps 14, 16. Even if the instantaneous measurement is filtered anddead zones are applied to overcome any jittering in the steering anglemeasurement resulting from the lane adjustments by the driver, theillumination will still not be optimal. Filtering and dead zoningoperations delay performance of a conventional AFS in the above roadscenarios. Moreover, conventional AFS performance is negatively affectedby many typical driving behaviors, for example lane changes. It isdesirable that the headlamps do not remain in their straight position inthis road scenario.

FIG. 3 shows, in the curve entry scenario, a first projected beampattern 72 for a conventional AFS system (in this scenario) and a secondbeam pattern 74 for the system 10, of the present invention. The firstbeam pattern 72 has a kink point 76 formed by the overlapping patternsfrom the first and second headlamp 14, 16. A kink point is an inflectionpoint in the low beam and may have a luminance value of around 1 Lux.Further, the kink point may be the location in the low beam where thelongitudinal axis of the vehicle intersects with the cut-off linebetween the high and the low beams. Ideally, the kink point is locatedin the center of the desired illumination area. Similarly, the secondbeam pattern 74 has kink point 78. The system 10 swivels the headlamps'beam by an angle φ such that the beam's kink point 74 is in the middleof the host vehicle lane, the lane occupied by the vehicle equipped withthe present system 10. In achieving this, the system 10 utilizes analgorithm that calculates the upcoming path data (such as curvaturedata) along the predicted vehicle path. The system 10 is configured toachieve the optimal visibility for travel in the host vehicle travellane, under the constraint that the glare is minimized, to the extentpossible, for oncoming traffic.

Unfortunately, turns in roads do not have a constant curvature. As aresult, swiveling a constant shape headlamp beam pattern to optimallycover the host vehicle lane can be challenging without glaring theoncoming traffic or increasing the visibility on one part of the lane atthe expense of the other part. Further, the geometry of some roads maypose particular challenges. For example in an S-curve road geometry, thedirection (sign) of the curvature changes quickly over a relativelyshort distance. Similar challenges arise when the vehicle changes lanes.

FIG. 4 shows a block diagram of the algorithm used by the controller 12for controlling the swivel mechanisms 18, 20 in accordance with thepresent invention. In block 102, the GPS position from block 104 isaugmented with the yaw rate measurement from block 106 and speedmeasurement from block 108. The GPS position, yaw rate, and/or vehiclespeed are used to calculate the vehicle position 110 in a globalcoordinate system. Using the map database 112, the map-matchingalgorithm 114 identifies the calculated vehicle position 116 on a map.The path prediction algorithm 118 analyzes the map relative tocalculated position and the direction of vehicle travel. The pathprediction algorithm 118 calculates the candidate list of the probableintended driving paths. From the list, the path prediction algorithm 118determines the most probable path by associating confidence scores toeach driving path on the list. Once the most likely path (MLP) isdetermined, the path prediction algorithm calculates the curvature ofthe MLP. The path data 120, including the resultant curvature values,are provided to the predictive adaptive frontlighting system (PAFS)algorithm block 122. The PAFS algorithm 122 uses the calculated pathdata 120 in order to determine the desired swiveling angle 124 of eachheadlamp 14,16. The desired swivel angles 124 are respectively providedto the swivel mechanisms 18, 20, as denoted by block 126.

Sometimes the confidence scores may be close between two or more drivingpaths. In this scenario, the resultant curvature values and other pathdata such as branching angle, distance to branching, are passed to thePAFS algorithm 122. The PAFS algorithm 122 uses the calculated pathsdata in order to determine the desired headlamp swiveling angles.

When the confidence scores between two paths are close, the PAFSalgorithm 122 may light the two possible road paths, the MLP and asecondary path (SP), one with one headlamp and one with anotherheadlamp. The PAFS algorithm 122 may calculate the swivel angle of eachheadlamp based on a function of the established confidence in each path,the curvature distribution of each path, the branching angle between thetwo paths, the distance between the vehicle position and the roadbranching point, and the time needed to reach the branch. The PAFSalgorithm approach may vary based on the confidence level of the MLP, asshown in FIG. 5.

In FIG. 5, the method 150 starts in block 152. In block 154 the PAFSalgorithm 122 receives confidence values and path data for the pathlist. In block 156 the system determines if the confidence level of themost likely path is high, for example above a threshold level eitherindependently or relative to the confidence values for other paths inthe path list. If the confidence level is not high the method 150follows line 158 to block 160. In block 160, the PAFS algorithm 122calculates the swivel angles of the right and headlamps separately as afunction of the branching angle between the MLP and the SP, the distanceto the branch, and the time to reach the branch. In block 162, theswivel angle for the right and left headlamps are clipped based on themaximum separation angle that headlamps are allowed. The method thenends in block 176.

Referring again to block 156, if the confidence level of the most likelypath is high, the method 150 follows line 164. In block 166, the PAFSalgorithm 122 separately calculates the swivel angle of the rightheadlamp and left headlamp as a function of the branching angle of themost likely path and the secondary path, the distance to the branch, andthe time to reach the branch. In block 168, the PAFS algorithm 122calculates a swivel angle that locates the kink point of the beampattern at the center of the host vehicle lane for the MLP. For theheadlamp associated with the MLP, the PAFS algorithm 122 calculates aweighted average of the swivel angle calculated in block 166 and theswivel angle calculated in block 168. For further clarification, if thePAFS algorithm 122 determines that the MLP is to the right of the secondpath, the PAFS algorithm 122 will calculate the weight average of theright headlamp, calculated in block 166, along with the swivel anglethat locates the kink point at the center of the host lane of the mostlikely path. The weight average angle is then applied to the associatedheadlamp, in this example, the right headlamp. For the headlamp notassociated with the most likely path, the left headlamp in the example,the individually calculated swivel angle from block 166 is applied, asdenoted in block 172. In block 174, the right and left headlamp anglesare clipped based on the maximum separation angle the headlamps areallowed. The left and right headlamps are then manipulated by the swivelmechanisms to effectuate the calculated swivel angles for the left andright headlamp and the method 150 ends as denoted on block 176.

Due to limitation in the map and GPS position accuracies, it is possiblethat the map-matching algorithm 114 will have difficulty in correctlyplacing the vehicle on the map in the vicinity of the branching point.Therefore, when the vehicle position is in the vicinity of the branchingpoint, only one headlamp changes its swiveling angle after branching.This headlamp is the one that is in the direction of the single pathafter branching. Another proposal to decrease the effect of themap-matching error is to continue swiveling the headlamps by the sameswiveling angle before branching. Either way, there should be a timeperiod T when the system returns to the single path mode afterbranching.

To further understand the calculation of the right and left headlamp inblocks 160 and 166, the following example is provided. The exampleutilizes the following defined parameters: the distance to branch as R;the first branching angle β1; the second branching angles β2; thevehicle speed as V; the time to reach as TR; the left swiveling angle asα_(L); the right swiveling angle α_(R): and the maximum allowableseparation angle S.

Given β1 and β2, then β_(R) is defined as the maximum of β1 and β2, andβ_(L) is the minimum of β1 and β2. The swiveling angles α_(R) and α_(L)can be calculated from the following equations:

$\begin{matrix}{{\alpha_{R} = {{\beta_{R}\left\lbrack {\min\left( {1,\frac{l + {0.6V} - {\min\left( {{l + {0.6V}},R} \right)}}{10}} \right)} \right\rbrack}\left\lbrack \left( {\min\left( {1,{\left( {6 - {\min\left( {{TR},6} \right)}} \right)/3}} \right)} \right) \right\rbrack}}{\alpha_{L} = {{\beta_{L}\left\lbrack {\min\left( {1,\frac{l + {0.6V} - {\min\left( {{l + {0.6V}},R} \right)}}{10}} \right)} \right\rbrack}\left\lbrack \left( {\min\left( {1,{\left( {6 - {\min\left( {{TR},6} \right)}} \right)/3}} \right)} \right) \right\rbrack}}{{TR} = \frac{R}{V}}{{Also},\text{}{{{\alpha_{R} - \alpha_{L}}}<=S}}} & (1)\end{matrix}$Where l is the kink point distance. To understand how this equationworks, it is analyzed below term by term.

The two minimum terms (min(1) that exist in the right and in the leftswiveling equations have a value that is always between 0 and 1.Therefore, multiplication of those two terms will be between 0 and 1.This dictates that the value of α will be always less that or equal toβ. The first term indicates that as long as the value of the distance tobranch R is less than the kink point distance l, the right and the leftswiveling angles will be zero. The swivel angles should be zero becausethere is little benefit of swiveling the headlamps if the beam has notreached the branching point yet. This term also indicates that as thevehicle gets closer to the branch, the swiveling angle α increases asthe R decreases. The addition of the 0.6*V to the kink point distance lis to compensate for system latency. The second term is a timing term.The timing term dictates that as the time to reach branching decreases,the swiveling angle increases. The swiveling angle increase based ontime because of two reasons: the first one is that the need to swivelthe headlamps is lower if the speed is very low, and the second is thatmore confidence is established in the vehicle MLP as time elapses.

FIG. 6 shows a geometric representation 202 of the predicted path data204. The geometric representation 202 in the map database 112 isperformed using node points 206 and shape points 208. Every road segmentin the map starts and ends with a node point 206. Shape points 208 arelocated between node points 206 to capture the road segment geometry.The path prediction algorithm 118 determines the most likely drivingpath and calculates the curvature at each of the shape points 208 andnode points 206 within the range of the look ahead distance 210. Thepath prediction algorithm 118 also calculates the curvatures betweenshape points 208 based on a road model. In illustrative example, twentycurvature points 212 span over the look ahead distance 210 and theassociated distances of each curvature point 212 measured from thevehicle position 214 are provided to the PAFS algorithm 122. The PAFSalgorithm 122 uses some of these twenty curvature points 212 anddistance values to determine the optimal headlamp swiveling angle 124.It will be readily appreciated that a greater or lesser number ofcurvature points 212 could be used.

As stated above, the path prediction module 118 provides, for example,twenty curvature points 212 along the look ahead distance 210. Inaddition, the path prediction module 118 also provides other data suchas the corresponding geometric distance, travel distance, coordinateposition and vehicle heading angle, which for example, is measured fromnorth.

In an optimal design, the headlamp beam 74 itself can be deformed tofollow the road geometry. In many implementations, however, the degreeof freedom for shaping the beam is not sufficient to achieve suchdeformation. Some solutions may therefore be sub-optimal. One approachis based on placing the beam's kink point 78, shown in FIG. 2, at adesired lateral distance from the center of the host vehicle lane.However, this approach is subject to the constraint that glare isminimized and visibility is uniformly distributed on the host vehiclelane.

Accordingly, the steps in operating a single path PAFS may include:

-   -   1—Calculate the nominal swiveling angle (φ) that will place the        kink point at a lateral distance D from the host vehicle lane        center.    -   2—Calculate the maximum swiveling angle (φ_(L)) that can be used        without creating glare to the oncoming traffic or badly        affecting the visibility distribution.    -   3—Calculate the desired swivel angle (φ_(f)) as the minimum of φ        and φ_(L) in the absolute sense or a weighted average of φ and        φ_(L). The weights given φ and φ_(L) may be a function of the        difference between the two angles.

The calculation of the swiveling angle φ will be based on the curvatureand distance data. As shown in FIG. 7, the headlamp beam 74 spans over anon-constant radius of curvature road segment. Even though the pathprediction algorithm thoroughly describes the curvature behavior betweenarea 301 and 302, for simplicity of this example, we will assume thatthe curvature (ρ) is linearly changing between area 301 and area 302.The area 301 is contained between curvature points cp1, cp2 and cp3.Since the separation between the curvature points is a function ofspeed, this separation of points will help in overcoming the delay.Therefore,

$\begin{matrix}{\rho_{v} = \frac{\rho_{1} + \rho_{2} + \rho_{3}}{3}} & (1)\end{matrix}$where

-   -   ρ is the curvature at the vehicle position,    -   ρ_(v) is the curvature at cp₁,    -   ρ₂ is the curvature at cp₂; and    -   ρ₃ is the curvature at cp₃.

The area 302 is contained between curvature point cp_(l) (ρ_(l)) atgeometric distance of l (distance to the kink point 304) and the nexttwo cp's (ρ_(next1), ρ_(next2)). Since the separation between the cp'sis a function of speed, this will help in overcoming the delay.Therefore,

$\begin{matrix}{\rho_{k} = \frac{\rho_{l} + \rho_{{next}\; 1} + \rho_{{next}\; 2}}{3}} & (2)\end{matrix}$where

-   -   ρ_(k) is the curvature at the kink point,    -   ρ_(l) is the curvature at the distance,    -   ρ_(next1) is the curvature at the first curvature point beyond        cp_(l) (cp_(next1)); and    -   ρ_(next2) is the curvature at the second curvature point beyond        cp_(l) (cp_(next2)).        The curvature change rate (K) between area 301 and area 302 is        calculated as

$\begin{matrix}{\kappa = \frac{\rho_{k} - \rho_{v}}{l}} & (3)\end{matrix}$where l is the geometric distance between cp₁ and cp_(l).

If the kink point 304 is to be placed at a lateral distance D (positiveto the left) from the center of the road, the angle φ can beapproximately related the heading angle (ψ) of the vehicle with respectto the road, the geometric distance l, the curvature ρ_(k) and ρ_(v) asfollows:

$\begin{matrix}{\phi = {{- \psi} + {\frac{\rho_{v}}{3}l} + {\frac{\rho_{k}}{6}l} - \frac{D}{l}}} & (4)\end{matrix}$where,

-   -   φ is the nominal swivel angle;    -   ψ is a heading angle;    -   ρ_(v) is the curvature at the vehicle position;    -   ρ_(k) is the curvature at the kink point;    -   l is the distance to the kink point from the vehicle; and    -   D is a predefined lateral distance from center of the host lane.        In this illustrative example, ψ is positive to the right.

The calculation of the heading angle ψ enables the PAFS algorithm 122 tocompensate for driving behavior, for example when performing a lanechange. Using a conventional AFS, the swiveling direction of headlamps'beam would follow the steering angle, and, as a result, the upcomingtraffic would be glared and the visibility of the road ahead would bereduced.

The calculation of the heading angle can be achieved by using thefollowing equation:ψ=ψ_(gps)−ψ_(cp1) _(—) ₂  (5)Where ψ_(gps) is the GPS position heading angle measured from the northaxis, and ψ_(cp1) _(—) ₂ is the heading angle measured from the northaxis of the vector that passes through cp1 and cp2.

D represents a protection zone against the imperfect accuracy of thecurvature values. For example, in the left curve scenario, it is desiredto set the lateral distance D such that the kink point 304 will beplaced to the outside part of the curve (to the right of the lanecenter). This will minimize the glaring effect due to any errors in thecurvature values.

To improve uniformity in visibility and avoid glare, the maximum swivelangle (φ_(L)) is calculated. Introducing the term ρ_(L) will help tomake the visibility almost uniform along the driving path and willdecrease the glare to oncoming traffic. However, to guarantee that thevisibility uniformity will not be negatively affected and that glarewill be controllable, the following approach may be utilized.

The controller 12 determines the curvature point between area 301 andarea 302 where the vertical line that equally divides the beam (beamcenter line 402) has its maximum lateral deviation 404 from the centerof the road 405, see FIGS. 7A-D.

As can be noticed from FIGS. 8A-D, the point of maximum deviation 406 isalways closer to the maximum of the curvatures at area 301 and area 302,if both curvatures have the same sign. Therefore, the point of maximumdeviation 406 can be approximated to be at a distance L where

$\begin{matrix}{L = {\frac{l}{2}\left( {1 + \frac{{\rho_{k}} - {\rho_{v}}}{{{\rho_{k} - \rho_{v}}} + {\max\left( {{\rho_{v}} \cdot {\rho_{k}}} \right)}}} \right)}} & (6)\end{matrix}$

The idea behind this formula is that when ρ_(v) is greater than ρ_(k)and both have the same sign, the point of maximum deviation will be lessthan l/2, which aligns with the concept illustrated in FIGS. 8A-D. Onthe other hand, if ρ_(k) is greater than ρ_(v) and they have the samesign, the point of maximum deviation will be greater than l/2. If thesign of the two curvatures differ, then the controller 12 sets ρ_(k)=0,i.e.

$\begin{matrix}{L = \frac{1}{4}} & (7)\end{matrix}$

Let us assume that a lateral deviation of D_(L) (for example, (⅕)th thelane width) is acceptable, then the angle threshold can be calculated

$\begin{matrix}{\phi_{L} = {{- \psi} + {\frac{\rho_{v}}{3}L} + {\frac{\rho_{L}}{6}L} - \frac{D_{L}}{L}}} & (8)\end{matrix}$

ρ_(L) is the curvature at the distance L. The final desired swivelingangle (φ_(f)) can be the minimum of φ and φ_(L).|φ_(j)|=min(|φ_(L)|·|φ|)  (9)

As a person skilled in the art will readily appreciate, the abovedescription is meant as an illustration of implementation of theprinciples this invention. This description is not intended to limit thescope or application of this invention in that the invention issusceptible to modification, variation and change, without departingfrom the spirit of this invention, as defined in the following claims.

1. A system for adaptive front lighting of a vehicle, the systemcomprising: a first headlamp and a second headlamp configured tocooperatively project a beam pattern; a first swivel mechanism coupledto the first headlamp to change a swivel angle of the first headlamp; asecond swivel mechanism coupled to the second headlamp to change aswivel angle of the second headlamp; and a controller configured todetermine a most likely path of the vehicle and a secondary path,wherein the controller is configured to calculate a first desired swivelangle of the first headlamp based on the most likely path of the vehicleand a second desired swivel angle of the second headlamp based on thesecondary path of the vehicle.
 2. The system according to claim 1,wherein the controller is configured to determine the first desiredswivel angle based on the branching angle of the most likely path of thevehicle, a distance to branch, and a time to reach the branch.
 3. Thesystem according to claim 2, wherein the first desired swivel angle iscalculated according to the relationship:$\alpha = {{\beta\left\lbrack {\min\left( {1,\frac{l + {0.6V} - {\min\left( {{l + {0.6V}},R} \right)}}{10}} \right)} \right\rbrack}\left\lbrack \left( {\min\left( {1,{\left( {6 - {\min\left( {{TR},6} \right)}} \right)/3}} \right)} \right) \right\rbrack}$where α is the first desired swivel angle; β is the branching angle ofthe most likely path; l is the kink point distance; V is the vehiclespeed; R is the distance to branch; and TR is time to branch.
 4. Thesystem according to claim 1, wherein the controller is configured tocalculate the second desired swivel angle based on the branching angleof the secondary likely path, a distance to branch, and a time to reachthe branch.
 5. The system according to claim 4, wherein the seconddesired swivel angle is calculated according to the relationship:$\alpha = {{\beta\left\lbrack {\min\left( {1,\frac{l + {0.6V} - {\min\left( {{l + {0.6V}},R} \right)}}{10}} \right)} \right\rbrack}\left\lbrack \left( {\min\left( {1,{\left( {6 - {\min\left( {{TR},6} \right)}} \right)/3}} \right)} \right) \right\rbrack}$where α is the second desired swivel angle; β is the branching angle ofthe secondary path; l is the kink point distance; V is the vehiclespeed; R is the distance to branch: and TR is time to branch.
 6. Thesystem according to claim 1, wherein the controller is configured tocalculate a nominal swivel angle such that a kink point generated by thefirst headlamp will be a predefined distance from a host vehicle lanecenter.
 7. The system according to claim 6, wherein the nominal swivelangle is calculated according to the relationship:$\phi = {{- \psi} + {\frac{\rho_{v}}{2}l} + {\frac{\rho_{k}}{6}l} - \frac{D}{l}}$where, φ is the nominal swivel angle; ψ is the heading angle; ρ_(v) isthe curvature at the vehicle position; ρ_(k) is the curvature at thekink point; l is the distance to the kink point; and D is a predefinedlateral distance.
 8. The system according to claim 6, wherein thecontroller is configured to calculate a weighted average angle of thefirst desired swivel angle and the nominal swivel angle.
 9. A method foradaptive front lighting of a vehicle, the method comprising: determininga most likely path; determining a secondary path; calculating a firstdesired swivel angle for a first vehicle headlamp based on the mostlikely path of the vehicle; and calculating a second desired swivelangle for a second vehicle headlamp based on the secondary path.
 10. Thesystem according to claim 9, wherein the controller is configured tocalculate the first desired swivel angle based on the branching angle ofthe most likely path, a distance to branch, and a time to reach thebranch.
 11. The system according to claim 10, wherein the first desiredswivel angle is calculated according to the relationship:$\alpha = {{\beta\left\lbrack {\min\left( {1,\frac{l + {0.6V} - {\min\left( {{l + {0.6V}},R} \right)}}{10}} \right)} \right\rbrack}\left\lbrack \left( {\min\left( {1,{\left( {6 - {\min\left( {{TR},6} \right)}} \right)/3}} \right)} \right) \right\rbrack}$where α is the first desired swivel angle; β is the branching angle ofthe most likely path; l is the kink point distance; V is the vehiclespeed; R is the distance to branch; and TR is time to branch.
 12. Thesystem according to claim 10, wherein the controller is configured tocalculate the second desired swivel angle based on the branching angleof the most likely path, a distance to branch, and a time to reach thebranch.
 13. The system according to claim 12, wherein the second desiredswivel angle is calculated according to the relationship:$\alpha = {{\beta\left\lbrack {\min\left( {1,\frac{l + {0.6V} - {\min\left( {{l + {0.6V}},R} \right)}}{10}} \right)} \right\rbrack}\left\lbrack \left( {\min\left( {1,{\left( {6 - {\min\left( {{TR},6} \right)}} \right)/3}} \right)} \right) \right\rbrack}$where α is the second desired swivel angle; β is the branching angle ofthe secondary path; l is the kink point distance; V is the vehiclespeed; R is the distance to branch; and TR is time to branch.
 14. Thesystem according to claim 12, wherein the controller is configured tocalculate a nominal swivel angle such that the kink point will be apredefined distance from a host vehicle center lane.
 15. The systemaccording to claim 14, wherein the nominal swivel angle is calculatedaccording to the relationship:$\phi = {{- \psi} + {\frac{\rho_{v}}{3}l} + {\frac{\rho_{k}}{6}l} - \frac{D}{l}}$where, φ is the nominal swivel angle; ψ is the heading angle; ρ_(v) isthe curvature at the vehicle position; ρ_(L) is the curvature at thekink point; l is the distance to the kink point; and D is a predefinedlateral distance.
 16. The system according to claim 14, wherein thecontroller is configured to calculate a weighted average angle of thefirst desired swivel angle and the nominal swivel angle.